1. Field of the Invention
Embodiments of the invention provide techniques for analyzing a sequence of video frames. More particularly, to analyzing and learning behavior based on streaming video data while filtering out environmental illumination effects.
2. Description of the Related Art
Some currently available video surveillance systems provide simple object recognition capabilities. For example, a video surveillance system may be configured to classify a group of pixels (referred to as a “blob”) in a given frame as being a particular object (e.g., a person or vehicle). Once identified, a “blob” may be tracked from frame-to-frame in order to follow the “blob” moving through the scene over time, e.g., a person walking across the field of vision of a video surveillance camera. Further, such systems may be configured to determine when an object has engaged in certain predefined behaviors. For example, the system may include definitions used to recognize the occurrence of a number of pre-defined events, e.g., the system may evaluate the appearance of an object classified as depicting a car (a vehicle-appear event) coming to a stop over a number of frames (a vehicle-stop event).
Environmental illumination changes can negatively affect a video surveillance system's ability to accurately distinguish foreground objects. Such changes may include, but are not limited to, clouds blocking the sunlight, shadows cast by objects during daytime, shadows and highlights caused by lumination fluctuations due to lack of ambient light, and shadows due to artificial light sources at night. In general, these shadows and highlights resulting from environmental illumination changes may be mistaken as foreground where the shadows and highlights differ in appearance from a background model image which depicts learned scene background. That is, the video surveillance system may generate false-positives by misclassifying pixels of shadows and/or highlights as foreground pixels.
Some conventional video surveillance systems suppress effects of environmental illumination based on the assumption that false-positive foreground pixels which represent shadows and highlights differ from their corresponding background pixels only in luminance, but not chromaticity, values. Such systems may classify foreground pixels having similar chromaticity but higher or lower luminance values than corresponding background pixels as false-positive foreground pixels. However, experience has shown that this approach may itself generate false-positives and false-negatives by incorrectly finding foreground pixels to be background pixels, and vice versa. Such false-positives may cause the system to erroneously remove pixels of foreground objects from the foreground pixels. Conversely, such false-negatives may cause the system to keep shadow and/or highlight pixels as foreground pixels.